Scaling Email Infrastructure at Enterprise Level

Building email infrastructure that scales to billions of messages requires careful architectural decisions from day one. At Nylas, we’ve learned that the key isn’t just handling volume—it’s maintaining reliability while keeping the developer experience simple.

The challenge with email APIs is that they sit at the intersection of complex protocols (SMTP, IMAP, MIME) and modern developer expectations. Teams want REST APIs, webhooks, and real-time sync. But underneath, you’re dealing with decades-old specifications and quirky mail server implementations.

Architecture Principles

Our approach focuses on three principles: abstract complexity without hiding control, fail gracefully with detailed errors, and optimize for the 99% use case while supporting edge cases. When you’re processing millions of requests daily, even small optimizations compound significantly.

Example: Rate Limiting Strategy

Here’s how we implement intelligent rate limiting that respects both our infrastructure limits and user needs:

interface RateLimitConfig {
  windowMs: number;
  maxRequests: number;
  strategy: 'sliding' | 'fixed';
}

class RateLimiter {
  private requests: Map<string, number[]> = new Map();

  async checkLimit(
    identifier: string,
    config: RateLimitConfig
  ): Promise<{ allowed: boolean; remaining: number }> {
    const now = Date.now();
    const windowStart = now - config.windowMs;
    
    const userRequests = this.requests.get(identifier) || [];
    const recentRequests = userRequests.filter(
      timestamp => timestamp > windowStart
    );

    if (recentRequests.length >= config.maxRequests) {
      return {
        allowed: false,
        remaining: 0,
      };
    }

    recentRequests.push(now);
    this.requests.set(identifier, recentRequests);

    return {
      allowed: true,
      remaining: config.maxRequests - recentRequests.length,
    };
  }
}

This sliding window approach ensures fair distribution of resources while maintaining high throughput.

Error Handling Pattern

When dealing with email protocols, errors are inevitable. We use a structured approach:

class EmailError extends Error {
  constructor(
    message: string,
    public code: string,
    public retryable: boolean,
    public details?: Record<string, unknown>
  ) {
    super(message);
    this.name = 'EmailError';
  }
}

// Usage example
try {
  await sendEmail(payload);
} catch (error) {
  if (error instanceof EmailError && error.retryable) {
    // Implement exponential backoff
    await retryWithBackoff(() => sendEmail(payload));
  } else {
    // Log and surface to user
    logger.error('Non-retryable error', { error });
    throw error;
  }
}

The key is providing enough context for developers to understand what went wrong, while abstracting away protocol-specific details they don’t need to know about.

Performance Optimization

For high-volume scenarios, we use connection pooling and request batching:

class ConnectionPool {
  private pool: Connection[] = [];
  private maxSize: number;

  async acquire(): Promise<Connection> {
    if (this.pool.length > 0) {
      return this.pool.pop()!;
    }
    return this.createConnection();
  }

  release(conn: Connection): void {
    if (this.pool.length < this.maxSize && conn.isHealthy()) {
      this.pool.push(conn);
    } else {
      conn.close();
    }
  }
}

These patterns, combined with careful monitoring and gradual rollout strategies, allow us to scale reliably while maintaining the simplicity developers expect.